半监督离散势理论在遥感影像变化检测中的应用

随着遥感技术的发展，遥感影像变化检测作为一种有效的技术手段，在环境监测、灾害救援等领域发挥了重要作用。然而地物复杂、标记困难等问题导致有效的变化检测存在一定的困难。本文提出了一种基于半监督离散势理论的遥感影像变化检测方法。该方法首先采用一种新的标记样本点的方法得到训练集，然后利用KNN方法构造复杂网络，最后对复杂网络中经典Wu-Huberman算法进行改进并划分网络。所得到的两个社团结构恰好对应了变化部分和不变部分。试验结果表明，基于半监督离散势理论的变化检测方法具有良好的变化检测性能。

Application of semi-supervised discrete potential theory in remote sensing image change detection

With the development of remote sensing technology, change detection for remote sensing image provides an effective method in environmental monitoring, disaster relief and many other fields. However, it is still a challenging problem to develop more effective change detection methods due to the complexity of ground-truth and the difficulty of labeling the samples and so on. This paper proposes a remote sensing image change detection method based on semi-supervised discrete potential theory. The suggested method first uses a new method to label the samples to get the training set, then constructs complex network by KNN approach. Finally, it improves the classical Wu-Huberman algorithm in complex network and divides the network. As a result, the obtained two community structures exactly correspond to the change part and the invariant part. Experimental results show that the change detection method based on semi-supervised discrete potential theory has perfect change detection performance.